16,038 research outputs found
Non-power-of-Two FFTs: Exploring the Flexibility of the Montium TP
Coarse-grain reconfigurable architectures, like the Montium TP, have proven to be a very successful approach for low-power and high-performance computation of regular digital signal processing algorithms. This paper presents the implementation of a class of non-power-of-two FFTs to discover the limitations and Flexibility of the Montium TP for less regular algorithms. A non-power-of-two FFT is less regular compared to a traditional power-of-two FFT. The results of the implementation show the processing time, accuracy, energy consumption and Flexibility of the implementation
Low-power Programmable Processor for Fast Fourier Transform Based on Transport Triggered Architecture
This paper describes a low-power processor tailored for fast Fourier
transform computations where transport triggering template is exploited. The
processor is software-programmable while retaining an energy-efficiency
comparable to existing fixed-function implementations. The power savings are
achieved by compressing the computation kernel into one instruction word. The
word is stored in an instruction loop buffer, which is more power-efficient
than regular instruction memory storage. The processor supports all
power-of-two FFT sizes from 64 to 16384 and given 1 mJ of energy, it can
compute 20916 transforms of size 1024.Comment: 5 pages, 4 figures, 1 table, ICASSP 2019 conferenc
High performance Python for direct numerical simulations of turbulent flows
Direct Numerical Simulations (DNS) of the Navier Stokes equations is an
invaluable research tool in fluid dynamics. Still, there are few publicly
available research codes and, due to the heavy number crunching implied,
available codes are usually written in low-level languages such as C/C++ or
Fortran. In this paper we describe a pure scientific Python pseudo-spectral DNS
code that nearly matches the performance of C++ for thousands of processors and
billions of unknowns. We also describe a version optimized through Cython, that
is found to match the speed of C++. The solvers are written from scratch in
Python, both the mesh, the MPI domain decomposition, and the temporal
integrators. The solvers have been verified and benchmarked on the Shaheen
supercomputer at the KAUST supercomputing laboratory, and we are able to show
very good scaling up to several thousand cores.
A very important part of the implementation is the mesh decomposition (we
implement both slab and pencil decompositions) and 3D parallel Fast Fourier
Transforms (FFT). The mesh decomposition and FFT routines have been implemented
in Python using serial FFT routines (either NumPy, pyFFTW or any other serial
FFT module), NumPy array manipulations and with MPI communications handled by
MPI for Python (mpi4py). We show how we are able to execute a 3D parallel FFT
in Python for a slab mesh decomposition using 4 lines of compact Python code,
for which the parallel performance on Shaheen is found to be slightly better
than similar routines provided through the FFTW library. For a pencil mesh
decomposition 7 lines of code is required to execute a transform
Plasma simulation using the massively parallel processor
Two dimensional electrostatic simulation codes using the particle-in-cell model are developed on the Massively Parallel Processor (MPP). The conventional plasma simulation procedure that computes electric fields at particle positions by means of a gridded system is found inefficient on the MPP. The MPP simulation code is thus based on the gridless system in which particles are assigned to processing elements and electric fields are computed directly via Discrete Fourier Transform. Currently, the gridless model on the MPP in two dimensions is about nine times slower that the gridded system on the CRAY X-MP without considering I/O time. However, the gridless system on the MPP can be improved by incorporating a faster I/O between the staging memory and Array Unit and a more efficient procedure for taking floating point sums over processing elements. The initial results suggest that the parallel processors have the potential for performing large scale plasma simulations
A Scalable Correlator Architecture Based on Modular FPGA Hardware, Reuseable Gateware, and Data Packetization
A new generation of radio telescopes is achieving unprecedented levels of
sensitivity and resolution, as well as increased agility and field-of-view, by
employing high-performance digital signal processing hardware to phase and
correlate large numbers of antennas. The computational demands of these imaging
systems scale in proportion to BMN^2, where B is the signal bandwidth, M is the
number of independent beams, and N is the number of antennas. The
specifications of many new arrays lead to demands in excess of tens of PetaOps
per second.
To meet this challenge, we have developed a general purpose correlator
architecture using standard 10-Gbit Ethernet switches to pass data between
flexible hardware modules containing Field Programmable Gate Array (FPGA)
chips. These chips are programmed using open-source signal processing libraries
we have developed to be flexible, scalable, and chip-independent. This work
reduces the time and cost of implementing a wide range of signal processing
systems, with correlators foremost among them,and facilitates upgrading to new
generations of processing technology. We present several correlator
deployments, including a 16-antenna, 200-MHz bandwidth, 4-bit, full Stokes
parameter application deployed on the Precision Array for Probing the Epoch of
Reionization.Comment: Accepted to Publications of the Astronomy Society of the Pacific. 31
pages. v2: corrected typo, v3: corrected Fig. 1
A hybrid MPI-OpenMP scheme for scalable parallel pseudospectral computations for fluid turbulence
A hybrid scheme that utilizes MPI for distributed memory parallelism and
OpenMP for shared memory parallelism is presented. The work is motivated by the
desire to achieve exceptionally high Reynolds numbers in pseudospectral
computations of fluid turbulence on emerging petascale, high core-count,
massively parallel processing systems. The hybrid implementation derives from
and augments a well-tested scalable MPI-parallelized pseudospectral code. The
hybrid paradigm leads to a new picture for the domain decomposition of the
pseudospectral grids, which is helpful in understanding, among other things,
the 3D transpose of the global data that is necessary for the parallel fast
Fourier transforms that are the central component of the numerical
discretizations. Details of the hybrid implementation are provided, and
performance tests illustrate the utility of the method. It is shown that the
hybrid scheme achieves near ideal scalability up to ~20000 compute cores with a
maximum mean efficiency of 83%. Data are presented that demonstrate how to
choose the optimal number of MPI processes and OpenMP threads in order to
optimize code performance on two different platforms.Comment: Submitted to Parallel Computin
Accelerated Modeling of Near and Far-Field Diffraction for Coronagraphic Optical Systems
Accurately predicting the performance of coronagraphs and tolerancing optical
surfaces for high-contrast imaging requires a detailed accounting of
diffraction effects. Unlike simple Fraunhofer diffraction modeling, near and
far-field diffraction effects, such as the Talbot effect, are captured by
plane-to-plane propagation using Fresnel and angular spectrum propagation. This
approach requires a sequence of computationally intensive Fourier transforms
and quadratic phase functions, which limit the design and aberration
sensitivity parameter space which can be explored at high-fidelity in the
course of coronagraph design. This study presents the results of optimizing the
multi-surface propagation module of the open source Physical Optics Propagation
in PYthon (POPPY) package. This optimization was performed by implementing and
benchmarking Fourier transforms and array operations on graphics processing
units, as well as optimizing multithreaded numerical calculations using the
NumExpr python library where appropriate, to speed the end-to-end simulation of
observatory and coronagraph optical systems. Using realistic systems, this
study demonstrates a greater than five-fold decrease in wall-clock runtime over
POPPY's previous implementation and describes opportunities for further
improvements in diffraction modeling performance.Comment: Presented at SPIE ASTI 2018, Austin Texas. 11 pages, 6 figure
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